def run(geoentities):
try:
print "starting ai.predict"
connection.close()
start = datetime.now()
classifier = DNNLinearCombinedClassifier(
model_dir=MODEL_DIR,
linear_feature_columns=wide_columns,
dnn_feature_columns=deep_columns,
dnn_hidden_units=[100,50]
)
print "classifier:", classifier
print "creating the classifier took", (datetime.now() - start).total_seconds(), "seconds"
df = get_fake_df()
print "about to populate data frame for prediction"
start_df = datetime.now()
for index, geoentity in enumerate(geoentities):
place_id = geoentity.place_id
name = geoentity.target
feature_admin_levels = set([g.admin_level for g in geoentities if g.admin_level and g.target == name])
if feature_admin_levels:
lowest_admin_level = min(feature_admin_levels)
else:
lowest_admin_level = -99
population = g.population
is_highest_population = population and population == max([g.population for g in geoentities if g.target == name]) or False
admin_level = geoentity.admin_level
df['admin_level'].append(str(geoentity.admin_level or "None"))
df['cluster_frequency'].append(geoentity.cluster_frequency or 0)
df['country_code'].append(geoentity.country_code or "UNKNOWN")
df['country_rank'].append(geoentity.country_rank or 999)
df['edit_distance'].append(str(geoentity.edit_distance))
df['has_mpoly'].append(str(geoentity.has_mpoly or False))
df['has_pcode'].append(str(geoentity.has_pcode or False))
df['is_country'].append(str(admin_level == 0))
df['is_lowest_admin_level'].append(str(lowest_admin_level == g.admin_level))
df['is_highest_population'].append(str(is_highest_population))
df['median_distance'].append(geoentity.median_distance_from_all_other_points)
df['matches_topic'].append(str(geoentity.matches_topic or "False"))
df['population'].append(geoentity.population)
df['popularity'].append(geoentity.popularity)
print "populating df took", ((datetime.now() - start_df).total_seconds() / 60), "minutes"
for index, row in enumerate(classifier.predict_proba(input_fn=lambda: input_fn(df))):
geoentities[index].probability = row[1]
except Exception as e:
fail("EXCPETION in scripts.ai.predict.run: " + str(e))
